On the universality of spectroscopic constants of diatomic molecules

TL;DR

This paper demonstrates that key spectroscopic constants of diatomic molecules are universally related and can be accurately predicted using machine learning, regardless of the molecule's specific bond, but depend on atomic group and period.

Contribution

It reveals universal relationships among diatomic spectroscopic constants and introduces a machine learning method for accurate predictions based on atomic properties.

Findings

Spectroscopic constants are universally related across diatomic molecules.

Predictions of constants achieve accuracy within 5% using atomic group and period.

Universal relationships hold across different electronic structure calculation methods.

Abstract

We show, through a machine learning approach, that the equilibrium distance, harmonic vibrational frequency, and binding energy of diatomic molecules are universally related. In particular, the relationships between spectroscopic constants are valid independently of the molecular bond. However, they depend strongly on the group and period of the constituent atoms. As a result, we show that by employing the group and period of atoms within a molecule, the spectroscopic constants are predicted with an accuracy of $\lesssim 5\%$. Finally, the same universal relationships are satisfied when spectroscopic constants from {\it ab initio} and density functional theory (DFT) electronic structure methods are employed.